Python 3.11.0 | packaged by conda-forge | (main, Jan 16 2023, 14:12:30) [MSC v.1916 64 bit (AMD64)]
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IPython 8.12.2 -- An enhanced Interactive Python. Type '?' for help.
MEASURE_FOLDER = os.path.join(EXP_FOLDER, 'loadingRatePAEffect2')
data = []
for rel_path in os.listdir(MEASURE_FOLDER):
run_path = os.path.join(MEASURE_FOLDER, rel_path)
filename = os.path.join(run_path, 'data.csv')
settingsname = os.path.join(run_path, 'Settings.txt')
try:
dh1 = MTdataHost(SAMPLE_RATE)
dh1.loadCATdata(fileName=filename, settingsName=settingsname)
dh1.timeDeload=0
dh1.timeReload=0
dh1.setCATtiming()
dh1.tLoad = dh1.tLoad - dh1.timeBaseline
dh1.tBaseline = dh1.tBaseline - dh1.timeBaseline
dh1.setBaseline(0.002)
dh1.setLoading(0.002)
dh1.motR=0.1
initFit, initX = dh1.setInitialLoad(0.01)
ind1 = int(dh1.initTime[0]*2000)
ind2 = ind1 + len(dh1.initTime)
plt.plot(dh1.initTime, dh1.voltage[ind1:ind2+1][:len(dh1.initTime)])
plt.plot(dh1.initTime, initFit[2])
plt.title(f'Loading Rate = {dh1.initMOTR:.2e}, MOT SS = {dh1.motSS:.2e}')
plt.show()
plt.close()
data.append({'R':dh1.initMOTR, 'MOTSS':dh1.motSS})
except Exception:
pass
data = pd.DataFrame(data)
index 0 is out of bounds for axis 0 with size 0 Exception in setLoading :(
index 0 is out of bounds for axis 0 with size 0 Exception in setLoading :(
index 0 is out of bounds for axis 0 with size 0 Exception in setLoading :(
index 0 is out of bounds for axis 0 with size 0 Exception in setLoading :(
index 0 is out of bounds for axis 0 with size 0 Exception in setLoading :(
index 0 is out of bounds for axis 0 with size 0 Exception in setLoading :(
index 0 is out of bounds for axis 0 with size 0 Exception in setLoading :(
MEASURE_FOLDER = os.path.join(EXP_FOLDER, 'loadingRatePAEffect2')
data = []
for rel_path in os.listdir(MEASURE_FOLDER):
run_path = os.path.join(MEASURE_FOLDER, rel_path)
filename = os.path.join(run_path, 'data.csv')
settingsname = os.path.join(run_path, 'Settings.txt')
try:
dh1 = MTdataHost(SAMPLE_RATE)
dh1.loadCATdata(fileName=filename, settingsName=settingsname)
dh1.timeDeload=0
dh1.timeReload=0
dh1.setCATtiming()
dh1.tLoad = dh1.tLoad - dh1.timeBaseline
dh1.tBaseline = dh1.tBaseline - dh1.timeBaseline
dh1.setBaseline(0.002)
dh1.setLoading(0.002)
initFit, initX = dh1.setInitialLoad(0.01)
ind1 = int(dh1.initTime[0]*2000)
ind2 = ind1 + len(dh1.initTime)
plt.plot(dh1.initTime, dh1.voltage[ind1:ind2+1][:len(dh1.initTime)])
plt.plot(dh1.initTime, initFit[2])
plt.title(f'Loading Rate = {dh1.initMOTR:.2e}, MOT SS = {dh1.motSS:.2e}')
plt.show()
plt.close()
data.append({'R':dh1.initMOTR, 'MOTSS':dh1.motSS})
except Exception:
pass
data = pd.DataFrame(data)
index 0 is out of bounds for axis 0 with size 0 Exception in setLoading :(
index 0 is out of bounds for axis 0 with size 0 Exception in setLoading :(
index 0 is out of bounds for axis 0 with size 0 Exception in setLoading :(
index 0 is out of bounds for axis 0 with size 0 Exception in setLoading :(
index 0 is out of bounds for axis 0 with size 0 Exception in setLoading :(
index 0 is out of bounds for axis 0 with size 0 Exception in setLoading :(
index 0 is out of bounds for axis 0 with size 0 Exception in setLoading :(
data
| R | MOTSS | |
|---|---|---|
| 0 | 0.019687 | 0.142943 |
| 1 | 0.018433 | 0.160811 |
| 2 | 0.018179 | 0.172178 |
| 3 | 0.018500 | 0.146471 |
| 4 | 0.010104 | 0.072018 |
| 5 | 0.008487 | 0.101331 |
| 6 | 0.009577 | 0.081486 |
MEASURE_FOLDER = os.path.join(EXP_FOLDER, 'loadingRatePAEffect2')
data = []
for rel_path in os.listdir(MEASURE_FOLDER):
run_path = os.path.join(MEASURE_FOLDER, rel_path)
filename = os.path.join(run_path, 'data.csv')
settingsname = os.path.join(run_path, 'Settings.txt')
try:
dh1 = MTdataHost(SAMPLE_RATE)
dh1.loadCATdata(fileName=filename, settingsName=settingsname)
dh1.timeDeload=0
dh1.timeReload=0
dh1.setCATtiming()
dh1.tLoad = dh1.tLoad - dh1.timeBaseline
dh1.tBaseline = dh1.tBaseline - dh1.timeBaseline
dh1.setBaseline(0.002)
dh1.setLoading(0.002)
initFit, initX = dh1.setInitialLoad(0.01)
ind1 = int(dh1.initTime[0]*2000)
ind2 = ind1 + len(dh1.initTime)
plt.plot(dh1.initTime, dh1.voltage[ind1:ind2+1][:len(dh1.initTime)])
plt.plot(dh1.initTime, initFit[2])
plt.title(f'Loading Rate = {dh1.initMOTR:.2e}, MOT SS = {dh1.motSS:.2e}')
plt.show()
plt.close()
data.append({'R':dh1.initMOTR, 'MOTSS':dh1.motSS, 'Gamma':dh1.motR})
except Exception:
pass
data = pd.DataFrame(data)
index 0 is out of bounds for axis 0 with size 0 Exception in setLoading :(
index 0 is out of bounds for axis 0 with size 0 Exception in setLoading :(
index 0 is out of bounds for axis 0 with size 0 Exception in setLoading :(
index 0 is out of bounds for axis 0 with size 0 Exception in setLoading :(
index 0 is out of bounds for axis 0 with size 0 Exception in setLoading :(
index 0 is out of bounds for axis 0 with size 0 Exception in setLoading :(
index 0 is out of bounds for axis 0 with size 0 Exception in setLoading :(
data
| R | MOTSS | Gamma | |
|---|---|---|---|
| 0 | 0.019687 | 0.142943 | 0.138208 |
| 1 | 0.018433 | 0.160811 | 0.114580 |
| 2 | 0.018179 | 0.172178 | 0.117944 |
| 3 | 0.018500 | 0.146471 | 0.145065 |
| 4 | 0.010104 | 0.072018 | 0.155786 |
| 5 | 0.008487 | 0.101331 | 0.097917 |
| 6 | 0.009577 | 0.081486 | 0.136252 |
MEASURE_FOLDER = os.path.join(EXP_FOLDER, 'varPAGranularOct5')
df = get_data_frame(MEASURE_FOLDER,
plot=False,
cache_all=True)
df.drop(columns=['betaPAErr'], inplace=True)
df.dropna(inplace=True)
df = df[df['ratio']<1.2]
groupbyKey = 'pump_reference'
titleKey = 'pump_AOM_freq'
df_grouped = df.groupby(by=groupbyKey)
min_ratios = df_grouped['ratio'].min()
groups = dict(list(df_grouped))
dfs = [df for df in groups.values()]
# plotting ratio vs freq
max_freqs = [384182.5]*len(dfs)
zipped_data = list(zip(dfs, max_freqs))
fig, ax = plt.subplots()
for i, (df, max_freq) in enumerate(zipped_data[:]):
data = df
freqs = ((max_freq-PUMP_FREQUENCY)-(data['tempV']-data['tempV'].min())*FREQVSVOLT- (data['currV']-data['currV'].min())*FREQVSCURR)
ax=plot_spline_fit(ax, x=freqs, y=data['ratio'], yerr=data['ratioErr'],scolor=f'C{i}', mfc=f'C{i}',color=f'C{i}', s=0.0, ms=5, figsize=(10, 10), label=f"{groupbyKey} = { data.iloc[10][groupbyKey] :.2f}", linewidth=2.5)
plt.legend()
plt.savefig(os.path.join(MEASURE_FOLDER, 'lossFeatures.png'))
plt.title(f'Loss Features, {titleKey} = {data[titleKey].mean():.2f}', **titledict)
plt.show()
plt.close()
#---------------------------------------------------
# x = [df[groupbyKey].mean() for df in dfs]
# y = [(df['ratio'].max() - df['ratio'].min()) for df in dfs]
# plt.plot( x, y ,'-o')
# plt.xlabel(groupbyKey)
# plt.ylabel(r'SNR $ = V_{ss, off} - V_{ss, on}$ ')
# plt.title(f'SNR Plot, {titleKey} = {df[titleKey].mean():.2f}', **titledict)
# plt.show()
# plt.savefig(join(MEASURE_FOLDER, 'SNRplot.png'), dpi=200)
# plt.close()
#---------------------------------------------------
for i, df in enumerate(dfs[:]):
data = df
freqs = ((max_freqs[0]-PUMP_FREQUENCY)-(data['tempV']-data['tempV'].min())*FREQVSVOLT- (data['currV']-data['currV'].min())*FREQVSCURR)
betaPAs = [a for a,b in sorted(zip(data['betaPA'], freqs), key=lambda pair:pair[1])]
freqs = sorted(freqs)
plt.plot(freqs, betaPAs, 'o-', ms=5, label=f"{groupbyKey}={data.iloc[10][groupbyKey]:.2f}")
plt.legend()
plt.xlabel(r'$\Delta$ (GHz)')
plt.ylabel(r'$\beta_{\mathrm{eff}}$ ')
plt.savefig(join(MEASURE_FOLDER, 'betaVsFreq.png'), dpi=200)
plt.title(f'2-body Decay Plot, {titleKey} = {df[titleKey].mean():.2f}', **titledict)
plt.show()
plt.close()
100%|██████████| 331/331 [03:39<00:00, 1.51it/s]
max_freqs = [384182.5]*len(dfs)
zipped_data = list(zip(dfs, max_freqs))[1:]
fig, ax = plt.subplots()
for i, (df, max_freq) in enumerate(zipped_data[:]):
data = df
freqs = ((max_freq-PUMP_FREQUENCY)-(data['tempV']-data['tempV'].min())*FREQVSVOLT- (data['currV']-data['currV'].min())*FREQVSCURR)
ax=plot_spline_fit(ax, x=freqs, y=data['ratio'], yerr=data['ratioErr'],scolor=f'C{i}', mfc=f'C{i}',color=f'C{i}', s=0.0, ms=5, figsize=(10, 10), label=f"{groupbyKey} = { data.iloc[10][groupbyKey] :.2f}", linewidth=2.5)
plt.legend()
plt.savefig(os.path.join(MEASURE_FOLDER, 'lossFeatures.png'))
plt.title(f'Loss Features, {titleKey} = {data[titleKey].mean():.2f}', **titledict)
plt.show()
plt.close()
#---------------------------------------------------
# x = [df[groupbyKey].mean() for df in dfs]
# y = [(df['ratio'].max() - df['ratio'].min()) for df in dfs]
# plt.plot( x, y ,'-o')
# plt.xlabel(groupbyKey)
# plt.ylabel(r'SNR $ = V_{ss, off} - V_{ss, on}$ ')
# plt.title(f'SNR Plot, {titleKey} = {df[titleKey].mean():.2f}', **titledict)
# plt.show()
# plt.savefig(join(MEASURE_FOLDER, 'SNRplot.png'), dpi=200)
# plt.close()
#---------------------------------------------------
for i, df in enumerate(dfs[:]):
data = df
freqs = ((max_freqs[0]-PUMP_FREQUENCY)-(data['tempV']-data['tempV'].min())*FREQVSVOLT- (data['currV']-data['currV'].min())*FREQVSCURR)
betaPAs = [a for a,b in sorted(zip(data['betaPA'], freqs), key=lambda pair:pair[1])]
freqs = sorted(freqs)
plt.plot(freqs, betaPAs, 'o-', ms=5, label=f"{groupbyKey}={data.iloc[10][groupbyKey]:.2f}")
plt.legend()
plt.xlabel(r'$\Delta$ (GHz)')
plt.ylabel(r'$\beta_{\mathrm{eff}}$ ')
plt.savefig(join(MEASURE_FOLDER, 'betaVsFreq.png'), dpi=200)
plt.title(f'2-body Decay Plot, {titleKey} = {df[titleKey].mean():.2f}', **titledict)
plt.show()
plt.close()
dfs = dfs[1:]
max_freqs = [384182.5]*len(dfs)
zipped_data = list(zip(dfs, max_freqs))
fig, ax = plt.subplots()
for i, (df, max_freq) in enumerate(zipped_data[:]):
data = df
freqs = ((max_freq-PUMP_FREQUENCY)-(data['tempV']-data['tempV'].min())*FREQVSVOLT- (data['currV']-data['currV'].min())*FREQVSCURR)
ax=plot_spline_fit(ax, x=freqs, y=data['ratio'], yerr=data['ratioErr'],scolor=f'C{i}', mfc=f'C{i}',color=f'C{i}', s=0.0, ms=5, figsize=(10, 10), label=f"{groupbyKey} = { data.iloc[10][groupbyKey] :.2f}", linewidth=2.5)
plt.legend()
plt.savefig(os.path.join(MEASURE_FOLDER, 'lossFeatures.png'))
plt.title(f'Loss Features, {titleKey} = {data[titleKey].mean():.2f}', **titledict)
plt.show()
plt.close()
#---------------------------------------------------
# x = [df[groupbyKey].mean() for df in dfs]
# y = [(df['ratio'].max() - df['ratio'].min()) for df in dfs]
# plt.plot( x, y ,'-o')
# plt.xlabel(groupbyKey)
# plt.ylabel(r'SNR $ = V_{ss, off} - V_{ss, on}$ ')
# plt.title(f'SNR Plot, {titleKey} = {df[titleKey].mean():.2f}', **titledict)
# plt.show()
# plt.savefig(join(MEASURE_FOLDER, 'SNRplot.png'), dpi=200)
# plt.close()
#---------------------------------------------------
for i, df in enumerate(dfs[:]):
data = df
freqs = ((max_freqs[0]-PUMP_FREQUENCY)-(data['tempV']-data['tempV'].min())*FREQVSVOLT- (data['currV']-data['currV'].min())*FREQVSCURR)
betaPAs = [a for a,b in sorted(zip(data['betaPA'], freqs), key=lambda pair:pair[1])]
freqs = sorted(freqs)
plt.plot(freqs, betaPAs, 'o-', ms=5, label=f"{groupbyKey}={data.iloc[10][groupbyKey]:.2f}")
plt.legend()
plt.xlabel(r'$\Delta$ (GHz)')
plt.ylabel(r'$\beta_{\mathrm{eff}}$ ')
plt.savefig(join(MEASURE_FOLDER, 'betaVsFreq.png'), dpi=200)
plt.title(f'2-body Decay Plot, {titleKey} = {df[titleKey].mean():.2f}', **titledict)
plt.show()
plt.close()
max_freqs = [384182.5]*len(dfs)
zipped_data = list(zip(dfs, max_freqs))
fig, ax = plt.subplots()
for i, (df, max_freq) in enumerate(zipped_data[::2]):
data = df
freqs = ((max_freq-PUMP_FREQUENCY)-(data['tempV']-data['tempV'].min())*FREQVSVOLT- (data['currV']-data['currV'].min())*FREQVSCURR)
ax=plot_spline_fit(ax, x=freqs, y=data['ratio'], yerr=data['ratioErr'],scolor=f'C{i}', mfc=f'C{i}',color=f'C{i}', s=0.0, ms=5, figsize=(10, 10), label=f"{groupbyKey} = { data.iloc[10][groupbyKey] :.2f}", linewidth=2.5)
plt.legend()
plt.savefig(os.path.join(MEASURE_FOLDER, 'lossFeatures.png'))
plt.title(f'Loss Features, {titleKey} = {data[titleKey].mean():.2f}', **titledict)
plt.show()
plt.close()
#---------------------------------------------------
# x = [df[groupbyKey].mean() for df in dfs]
# y = [(df['ratio'].max() - df['ratio'].min()) for df in dfs]
# plt.plot( x, y ,'-o')
# plt.xlabel(groupbyKey)
# plt.ylabel(r'SNR $ = V_{ss, off} - V_{ss, on}$ ')
# plt.title(f'SNR Plot, {titleKey} = {df[titleKey].mean():.2f}', **titledict)
# plt.show()
# plt.savefig(join(MEASURE_FOLDER, 'SNRplot.png'), dpi=200)
# plt.close()
#---------------------------------------------------
for i, df in enumerate(dfs[::2]):
data = df
freqs = ((max_freqs[0]-PUMP_FREQUENCY)-(data['tempV']-data['tempV'].min())*FREQVSVOLT- (data['currV']-data['currV'].min())*FREQVSCURR)
betaPAs = [a for a,b in sorted(zip(data['betaPA'], freqs), key=lambda pair:pair[1])]
freqs = sorted(freqs)
plt.plot(freqs, betaPAs, 'o-', ms=5, label=f"{groupbyKey}={data.iloc[10][groupbyKey]:.2f}")
plt.legend()
plt.xlabel(r'$\Delta$ (GHz)')
plt.ylabel(r'$\beta_{\mathrm{eff}}$ ')
plt.savefig(join(MEASURE_FOLDER, 'betaVsFreq.png'), dpi=200)
plt.title(f'2-body Decay Plot, {titleKey} = {df[titleKey].mean():.2f}', **titledict)
plt.show()
plt.close()
dfs = dfs[2:]
max_freqs = [384182.5]*len(dfs)
zipped_data = list(zip(dfs, max_freqs))
fig, ax = plt.subplots()
for i, (df, max_freq) in enumerate(zipped_data[:]):
data = df
freqs = ((max_freq-PUMP_FREQUENCY)-(data['tempV']-data['tempV'].min())*FREQVSVOLT- (data['currV']-data['currV'].min())*FREQVSCURR)
ax=plot_spline_fit(ax, x=freqs, y=data['ratio'], yerr=data['ratioErr'],scolor=f'C{i}', mfc=f'C{i}',color=f'C{i}', s=0.0, ms=5, figsize=(10, 10), label=f"{groupbyKey} = { data.iloc[10][groupbyKey] :.2f}", linewidth=2.5)
plt.legend()
plt.savefig(os.path.join(MEASURE_FOLDER, 'lossFeatures.png'))
plt.title(f'Loss Features, {titleKey} = {data[titleKey].mean():.2f}', **titledict)
plt.show()
plt.close()
#---------------------------------------------------
# x = [df[groupbyKey].mean() for df in dfs]
# y = [(df['ratio'].max() - df['ratio'].min()) for df in dfs]
# plt.plot( x, y ,'-o')
# plt.xlabel(groupbyKey)
# plt.ylabel(r'SNR $ = V_{ss, off} - V_{ss, on}$ ')
# plt.title(f'SNR Plot, {titleKey} = {df[titleKey].mean():.2f}', **titledict)
# plt.show()
# plt.savefig(join(MEASURE_FOLDER, 'SNRplot.png'), dpi=200)
# plt.close()
#---------------------------------------------------
for i, df in enumerate(dfs[:]):
data = df
freqs = ((max_freqs[0]-PUMP_FREQUENCY)-(data['tempV']-data['tempV'].min())*FREQVSVOLT- (data['currV']-data['currV'].min())*FREQVSCURR)
betaPAs = [a for a,b in sorted(zip(data['betaPA'], freqs), key=lambda pair:pair[1])]
freqs = sorted(freqs)
plt.plot(freqs, betaPAs, 'o-', ms=5, label=f"{groupbyKey}={data.iloc[10][groupbyKey]:.2f}")
plt.legend()
plt.xlabel(r'$\Delta$ (GHz)')
plt.ylabel(r'$\beta_{\mathrm{eff}}$ ')
plt.savefig(join(MEASURE_FOLDER, 'betaVsFreq.png'), dpi=200)
plt.title(f'2-body Decay Plot, {titleKey} = {df[titleKey].mean():.2f}', **titledict)
plt.show()
plt.close()